PREPRINT - Proximal Newton Methods for X-Ray Imaging with Non-Smooth Regularization

Tao Ge; Umberto Villa; Ulugbek S. Kamilov; Joseph O'Sullivan

PREPRINT - Proximal Newton Methods for X-Ray Imaging with Non-Smooth Regularization

Tao Ge, Umberto Villa, Ulugbek S. Kamilov, Joseph O'Sullivan

University of Missouri-St. Louis

Research output: Contribution to journal › Article › peer-review

Original language	American English
Journal	arXiv
State	Published - Dec 3 2019

Disciplines

Engineering
Electrical and Computer Engineering

Cite this

@article{cb9d8bc3631c4d27afffedc81ce5856e,

title = "PREPRINT - Proximal Newton Methods for X-Ray Imaging with Non-Smooth Regularization",

author = "Tao Ge and Umberto Villa and Kamilov, \{Ulugbek S.\} and Joseph O'Sullivan",

note = "Non-smooth regularization is widely used in image reconstruction to eliminate the noise while preserving subtle image structures. In this work, we investigate the use of proximal Newton (PN) method to solve an optimization problem with a smooth data-fidelity term and total variation (TV) regularization arising from image reconstruction applications.",

year = "2019",

month = dec,

day = "3",

language = "American English",

journal = "arXiv",

}

TY - JOUR

T1 - PREPRINT - Proximal Newton Methods for X-Ray Imaging with Non-Smooth Regularization

AU - Ge, Tao

AU - Villa, Umberto

AU - Kamilov, Ulugbek S.

AU - O'Sullivan, Joseph

N1 - Non-smooth regularization is widely used in image reconstruction to eliminate the noise while preserving subtle image structures. In this work, we investigate the use of proximal Newton (PN) method to solve an optimization problem with a smooth data-fidelity term and total variation (TV) regularization arising from image reconstruction applications.

PY - 2019/12/3

Y1 - 2019/12/3

UR - https://arxiv.org/abs/1912.01738

M3 - Article

JO - arXiv

JF - arXiv

ER -

PREPRINT - Proximal Newton Methods for X-Ray Imaging with Non-Smooth Regularization

Disciplines

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